An Antiforgetting Model for Higher Vocational English Learning Using BP Neural Network

Abstract

Currently, high-quality vocational education is an important foundation for the aspirations of China to create massive professionals and talented employees. Because of these, the advancement of modern vocational education is becoming increasingly important in the current era. Aside from these benefits, the use of artificial intelligence expertise in higher vocational English learning can effectively solve the unscientific problem of learning plan formulation in higher vocational English learning, increase the level of scientific learning, and strengthen students' autonomous learning ability. As a result, this research provides a BP neural network technique based on the Ebbinghaus forgetting curve that can effectively complete sample incremental training. It achieves performance comparable to the complete sample batch training approach while using many fewer samples to train. From experimental work, it is clear that the suggested work can effectively improve the training efficiency of the BP neural network to provide learners with a more suitable learning plan.